Confusion matrix for rotation forest classifier (Acceleration sensor placed on ankle).

<p>Confusion matrix for rotation forest classifier (Acceleration sensor placed on ankle).</p

Confusion matrix for KNN classifier on features set FS5.

<p>Confusion matrix for KNN classifier on features set FS5.</p

Classification results of three classifiers using all acceleration sensors.

<p>Classification results of three classifiers using all acceleration sensors.</p

Classification results of three classifiers on FS5 features set.

<p>Classification results of three classifiers on FS5 features set.</p

Classification results of three classifiers using the acceleration sensor placed at dominant wrist.

<p>Classification results of three classifiers using the acceleration sensor placed at dominant wrist.</p

Number of instances per activity.

<p>Number of instances per activity.</p

Confusion matrix for rotation forest classifier (Acceleration sensor placed on chest).

<p>Confusion matrix for rotation forest classifier (Acceleration sensor placed on chest).</p

Comparison of performance with the reported results.

<p>Comparison of performance with the reported results.</p

Data_Sheet_2_Effects of changing riparian topography on the decline of ecological indicators along the drawdown zones of long rivers in China.docx

Riparian topographical features can drive a suite of ecological indicators (EIs) that shape the river ecosystem. The mechanisms that EIs reflect provide several ecosystem services. We know little about the responses of EIs (indicators of plant cover, regeneration, exotics, habitat, erosion, and stressors) to the changing stream-channel width, riparian width, and elevation of the lengthy drawdown zones (upstream, midstream, and downstream) of long rivers. We have discovered that changing topographical characteristics affect riparian buffer areas differently by using a rapid field-based method with 297 transects in inundated regions along the Yangtze River and other 36 linked tributaries in China. Changing stream-channel widths was most effective on downstream EIs and the least effective at midstream. The exotic parameters were the most affected (with a range of −0.36 < r < 0.401) by stream-channel widths, as determined using Pearson correlation (p < 0.05). In contrast, the changing riparian width had the uppermost impact on the upstream EIs and the lowermost impact downstream; riparian width had the most significant impact on habitat parameters (with r ≤ 0.787). The elevation followed the riparian width pattern and was negatively associated with habitat and exotics (r ≤ −0.645 and r ≤ −0.594) and positively correlated with regeneration (r ≤ 0.569). These results reaffirm the imperative need for studies on regionally dependent riparian areas maintained under the same management strategies regardless of their topographical features. Future policies should be formulated to enhance ecosystem service provision, promoting the sustainable use of extensive river ecosystems while considering EIs. Additionally, these future policies should acknowledge drawdown zone factors within the same river network. Furthermore, additional measures are imperative to conserve topographical features and prevent further destruction.</p

Time-series event-based prediction: an unsupervised learning framework based on genetic programming

In this paper, we propose an unsupervised learning framework based on Genetic Programming (GP) to predict the position of any particular target event (defined by the user) in a time-series. GP is used to automatically build a library of candidate temporal features. The proposed framework receives a training set S ¼ fðVaÞja ¼ 0 ng, where each Va is a time-series vector such that 8Va 2 S; Va ¼ fðxtÞjt ¼ 0 tmaxg where tmax is the size of the time-series. All Va 2 S are assumed to be generated from the same environment. The proposed framework uses a divide-and-conquer strategy for the training phase. The training process of the proposed framework works as follow. The user specifies the target event that needs to be predicted (e.g., Highest value, Second Highest value, ..., etc.). Then, the framework classifies the training samples into different Bins, where Bins ¼ fðbiÞji ¼ 0 tmaxg, based on the time-slot t of the target event in each Va training sample. Each bi 2 Bins will contain a subset of S. For each bi, the proposed framework further classifies its samples into statistically independent clusters. To achieve this, each bi is treated as an independent problem where GP is used to evolve programs to extract statistical features from each bi’s members and classify them into different clusters using the K-Means algorithm. At the end of the training process, GP is used to build an ‘event detector’ that receives an unseen time-series and predicts the time-slot where the target event is expected to occur. Empirical evidence on artificially generated data and real-world data shows that the proposed framework significantly outperforms standard Radial Basis Function Networks, standard GP system, Gaussian Process regression, Linear regression, and Polynomial Regression